{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fc77495b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "90190df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./职位数据.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "81353b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company_full_name</th>\n",
       "      <th>type</th>\n",
       "      <th>city_district</th>\n",
       "      <th>salary</th>\n",
       "      <th>education</th>\n",
       "      <th>work_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>长沙蜜獾信息科技有限公司</td>\n",
       "      <td>开发|测试|运维类/后端开发/Python</td>\n",
       "      <td>长沙/岳麓区</td>\n",
       "      <td>12k-15k</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>长沙蜜獾信息科技有限公司</td>\n",
       "      <td>开发|测试|运维类/后端开发/Python</td>\n",
       "      <td>长沙/岳麓区</td>\n",
       "      <td>10k-15k</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>湖南语亦云科技有限公司</td>\n",
       "      <td>开发|测试|运维类/数据开发/爬虫</td>\n",
       "      <td>长沙/长沙县</td>\n",
       "      <td>7k-14k</td>\n",
       "      <td>不限</td>\n",
       "      <td>1-3年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>湖南同有飞骥科技有限公司</td>\n",
       "      <td>开发|测试|运维类/后端开发/Python</td>\n",
       "      <td>长沙/岳麓区</td>\n",
       "      <td>12k-17k</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>湖南象盒网络科技有限公司</td>\n",
       "      <td>开发|测试|运维类/后端开发/后端开发</td>\n",
       "      <td>长沙/芙蓉区</td>\n",
       "      <td>8k-13k</td>\n",
       "      <td>大专</td>\n",
       "      <td>3-5年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>北京外企德科人力资源服务上海有限公司</td>\n",
       "      <td>开发|测试|运维类/后端开发/后端开发</td>\n",
       "      <td>长沙/岳麓区</td>\n",
       "      <td>15k-30k</td>\n",
       "      <td>本科</td>\n",
       "      <td>不限</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>北京外企德科人力资源服务上海有限公司</td>\n",
       "      <td>开发|测试|运维类/后端开发/Python</td>\n",
       "      <td>长沙/岳麓区</td>\n",
       "      <td>17k-25k</td>\n",
       "      <td>本科</td>\n",
       "      <td>不限</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>亚信科技（中国）有限公司</td>\n",
       "      <td>开发|测试|运维类/人工智能/算法工程师</td>\n",
       "      <td>长沙/岳麓区</td>\n",
       "      <td>15k-30k</td>\n",
       "      <td>硕士</td>\n",
       "      <td>3-5年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>山景智能（北京）科技有限公司</td>\n",
       "      <td>开发|测试|运维类/人工智能/算法工程师</td>\n",
       "      <td>长沙/开福区</td>\n",
       "      <td>15k-30k</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>广州思璞网络科技有限公司</td>\n",
       "      <td>开发|测试|运维类/后端开发/Python</td>\n",
       "      <td>长沙/岳麓区</td>\n",
       "      <td>18k-20k</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>广州海颐软件有限公司</td>\n",
       "      <td>开发|测试|运维类/运维/运维工程师</td>\n",
       "      <td>长沙/天心区</td>\n",
       "      <td>7k-14k</td>\n",
       "      <td>本科</td>\n",
       "      <td>1-3年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>湖南微步信息科技有限责任公司</td>\n",
       "      <td>产品|需求|项目类/产品经理/数据产品经理</td>\n",
       "      <td>长沙/岳麓区</td>\n",
       "      <td>15k-25k</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-10年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>湖南微步信息科技有限责任公司</td>\n",
       "      <td>开发|测试|运维类/后端开发/GO|Golang</td>\n",
       "      <td>长沙/岳麓区</td>\n",
       "      <td>16k-32k</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>亚信科技（中国）有限公司</td>\n",
       "      <td>开发|测试|运维类/前端开发/WEB前端</td>\n",
       "      <td>长沙/岳麓区</td>\n",
       "      <td>10k-18k</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>深圳市水务科技有限公司</td>\n",
       "      <td>开发|测试|运维类/人工智能/算法工程师</td>\n",
       "      <td>长沙/长沙县</td>\n",
       "      <td>10k-16k</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-5年</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     company_full_name                      type city_district   salary   \n",
       "0         长沙蜜獾信息科技有限公司     开发|测试|运维类/后端开发/Python        长沙/岳麓区  12k-15k  \\\n",
       "1         长沙蜜獾信息科技有限公司     开发|测试|运维类/后端开发/Python        长沙/岳麓区  10k-15k   \n",
       "2          湖南语亦云科技有限公司         开发|测试|运维类/数据开发/爬虫        长沙/长沙县   7k-14k   \n",
       "3         湖南同有飞骥科技有限公司     开发|测试|运维类/后端开发/Python        长沙/岳麓区  12k-17k   \n",
       "4         湖南象盒网络科技有限公司       开发|测试|运维类/后端开发/后端开发        长沙/芙蓉区   8k-13k   \n",
       "5   北京外企德科人力资源服务上海有限公司       开发|测试|运维类/后端开发/后端开发        长沙/岳麓区  15k-30k   \n",
       "6   北京外企德科人力资源服务上海有限公司     开发|测试|运维类/后端开发/Python        长沙/岳麓区  17k-25k   \n",
       "7         亚信科技（中国）有限公司      开发|测试|运维类/人工智能/算法工程师        长沙/岳麓区  15k-30k   \n",
       "8       山景智能（北京）科技有限公司      开发|测试|运维类/人工智能/算法工程师        长沙/开福区  15k-30k   \n",
       "9         广州思璞网络科技有限公司     开发|测试|运维类/后端开发/Python        长沙/岳麓区  18k-20k   \n",
       "10          广州海颐软件有限公司        开发|测试|运维类/运维/运维工程师        长沙/天心区   7k-14k   \n",
       "11      湖南微步信息科技有限责任公司     产品|需求|项目类/产品经理/数据产品经理        长沙/岳麓区  15k-25k   \n",
       "12      湖南微步信息科技有限责任公司  开发|测试|运维类/后端开发/GO|Golang        长沙/岳麓区  16k-32k   \n",
       "13        亚信科技（中国）有限公司      开发|测试|运维类/前端开发/WEB前端        长沙/岳麓区  10k-18k   \n",
       "14         深圳市水务科技有限公司      开发|测试|运维类/人工智能/算法工程师        长沙/长沙县  10k-16k   \n",
       "\n",
       "   education work_year  \n",
       "0         本科      3-5年  \n",
       "1         本科      3-5年  \n",
       "2         不限      1-3年  \n",
       "3         本科      3-5年  \n",
       "4         大专      3-5年  \n",
       "5         本科        不限  \n",
       "6         本科        不限  \n",
       "7         硕士      3-5年  \n",
       "8         本科      1-3年  \n",
       "9         本科      3-5年  \n",
       "10        本科      1-3年  \n",
       "11        本科     5-10年  \n",
       "12        本科      3-5年  \n",
       "13        本科      3-5年  \n",
       "14        本科      3-5年  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a12874e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     本科\n",
       "1     本科\n",
       "2     不限\n",
       "3     本科\n",
       "4     大专\n",
       "5     本科\n",
       "6     本科\n",
       "7     硕士\n",
       "8     本科\n",
       "9     本科\n",
       "10    本科\n",
       "11    本科\n",
       "12    本科\n",
       "13    本科\n",
       "14    本科\n",
       "Name: education, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['education']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "60d8b0b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'不限', '大专', '本科', '硕士'}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = set(data['education'])\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "81128916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['硕士', '大专', '不限', '本科']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = list(labels)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "654fbb58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "本科    0.800000\n",
       "不限    0.066667\n",
       "大专    0.066667\n",
       "硕士    0.066667\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sizes = pd.Series(data['education']).value_counts(normalize=True)\n",
    "sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "31495400",
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams['font.family'] = 'SimHei'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b722c217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(sizes, labels=labels, autopct='%.2f%%')\n",
    "plt.title('教育水平要求')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b105aaa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
